In underwriting insurance policies, the management of risk exposure and cost are essential to the profitability of the risk bearer. Risk bearers, or those responsible and/or impacted by the profitability of a risk bearer, can be reinsurers, primary insurance carriers, managing general underwriters (“MGU's”), managing general agents (“MGA's”), third party administrators (“TPA's”), program administrators, retail insurance agents and the respective clients and/or buyers of insurance.
Profitability of risk bearers, their affiliates, and clients is based on a number of different ratios and factors. For a reinsurer or primary insurance carrier, for example, the main measurement is what is known as a “Combined Ratio.” It is calculated according to the National Council of Compensation Insurance (“NCCI”) as a measure of the extent to which premium income covers a company's losses and expenses determined by adding together a company's loss ratio and its expense ratio. In addition to the unpredictability of a loss incident occurring and impacting the loss ratio, a number of other factors hinder the ability of risk bearers to accurately project loss and ultimately help them manage their portfolios.
It is the current practice of risk bearers, their affiliates and clients to establish a projection of liabilities and premiums to offset those liabilities at policy inception based on the characteristics of a given exposure. Risk bearers, their affiliates and clients consider the underlying risks such as expected payrolls, premium job classifications, state of employment, loss experience and other subjective considerations in setting expected premiums and expenses to offset expected losses. The profitability of the given risk is then set by the actuarial and underwriting units of the risk bearer for a period of twelve months.
Sophisticated models have been developed, based upon credible data pools, for use in generating these projections of liability and in projecting return on policyholder surplus. For example, the NCCI, currently the foremost actuarial resource and ratemaking authority in 36 states for workers' compensation, collects data for these 36 workers' compensation systems to better understand losses by occupation to then set the appropriate loss costs and rates charged for those occupations by primary insurance carriers and other risk bearers, their affiliates and clients. Other states perform these same functions independently of the NCCI, such as the Worker's Compensation Insurance Rating Board (“WCIRB”) in California and the New Jersey Compensation Rating Insurance Board (“NJCRIB”).
Due to the manner and timing of how workers' compensation insurance carriers report premiums, payrolls and losses to actuarial bureaus through unit statistical cards, data is not available until at a minimum 18 months after policy inception and every 12 months thereafter to understand the true combined ratio/profitability of the risk bearer. The visualization of trends and identification of outliers that impact combined ratio/profitability has always relied on retrospective rather than current data sources. As a result, the earliest information with any credibility is 18 months old at best and, due to other changes in the interim, potentially irrelevant to predicting what happens in the future on the line of insurance being contemplated.
Changes that occur during the twelve month policy period, such as rate changes, addition of locations and employees, payroll increases and decreases, job classification deviations, jurisdictional based changes or amendments and potential layoffs that may occur within the twelve month policy period, leads to potential pricing inadequacy because the denominator of the combined ratio/loss ratio, known as “earned premiums,” is unknown. Earned premium as defined by the NCCI is the portion of the premium that represents coverage already provided and is equal to actual reported payrolls by state and class code divided by 100 and then multiplied by the actual rate set by the carrier by state. As a result, each day that an insurance policy is in force would be a day of earned premium. This void of knowledge regarding earned premiums is an instrumental historical problem that has plagued the industry and has made the actual analysis and tracking of the loss ratios and ultimately the profitability of risk bearers, their affiliates or clients in a timely manner virtually impossible.
Nonetheless, the practice of generating projections of losses based upon retrospective and potentially outdated exposure data is employed because there is currently no credible source of actual underlying premium exposure that can be used to measure anticipated losses expense 30 to 60 days prior to a policy effective/renewal date. Additionally, the inability to analyze loss expense data on a more frequent basis than the typical standard of monthly only adds further to the speed and access to data problem. Because of the foregoing limitations on available premium exposure data, the profitability of an insurance program can also only been measured retrospectively by the bearer(s) of risk for that given insurance program at minimum 18 months in arrears of the program inception.
The actuarial models that exist to better understand projected profitability stop at the establishment of loss selection and do not revisit profitability for at least 18 months thereafter. Actuarial science sets expected profitability by pricing the premiums of an insurance policy at a level where it is able to make the targeted profitability projections within a reasonable amount of certainty as set by the Chief Actuarial Officer, Chief Underwriting Officer or other insurance professionals that have been given the authority to bind the risk bearer, their affiliates and clients to the potential liabilities of a twelve month policy.
The actuarial fellow that sets initial guidance on an insurance carrier's portfolio of business or a specific account has nothing that is credible to understand profitability outside of expected losses until a premium audit is performed. The denominator known as earned premiums is not known. Premium audits are always performed after the policy expiration and are typically completed 6 months after the policy's expiration.
It is often the situation that existing projection models are inadequate for managing exposure or profitability for an individual company. By way of example, Professional Employer Organization's (“PEO's”) which can effectively deliver products and services to a business of any size, historically have been the most attractive to small business owners. This has been the case because small business owners are more often unable to provide full time employees the same benefits to deal with important aspects of being an employer such as human resources, employer and employee compliance (ERISA, COBRA, FMLA etc), W-2 payroll administration and reporting, Federal and State payroll tax reporting, and, importantly, the procurement of employee related insurance plans (workers' compensation, employment practices liability, health, disability, life, 401K etc). With respect to workers' compensation insurance, the vast majority of PEO client companies provided such insurance are small companies that generate limited premiums and have relatively limited numbers of claims.
Because credible predictions of the future expectation of claims and associated loss expenses can only be obtained through the analysis of a larger data set of claims than what can be provided by smaller employers, i.e., hundreds to thousands of times the number of claims experienced by individual PEO clients, the ability to create a credible pricing model for a workers' compensation policy for the average PEO client company does not exist considering the lack of credibility of available data on an individual client basis. Thus, the way that a typical PEO client company is being priced and underwritten by the insurance marketplace is deficient and ultimately can and often does impact the profitability of risk bearers, their affiliates and clients.
As a result of the foregoing, the issuance of policies to PEO's have often been based on composite rates as a function of the established rating basis versus premium and understated ultimate expected losses and therefore the collateral needed to offset them. Because the same limited client data is used to evaluate the profitability of PEO policies, these policies have also suffered insufficient rate setting and inadequate supervision of premium growth. There have also been insufficient controls and direction to ensure that the product provided to the PEO was beneficial to all involved in the transaction. While administratively easier, this methodology has created the inability for the risk bearer, their affiliates and clients to know the underlying premium base offsetting expected losses.
What is needed therefore is a system and apparatus for allowing risk bearers, their affiliates and clients in an insurance environment, the ability to more accurately forecast and manage risk exposure, the ability to better set rates and pricing for the acquisition of an asset, and to continually and consistently measure the profitability of the asset as set by the bound insurance policy in a timely real-time basis.